Monitoring and Predicting CO2 Flooding Using Material Balance Equation
Bibliographic record
Abstract
Abstract Operating a CO2 flooding scheme successfully requires the capacity to get accurate information of the reservoir dynamic performance and the fluids injected. Though some numerical simulation studies have been conducted, the complicated drive mechanisms and actual reservoir performance have not been fully understood. Thus, there is a strong industrial need to develop models from different perspectives that may be obscured by current simulators to provide valuable and complementary insights into the reservoir performance during CO2 flooding process. The objective of this study is to develop models using material balance equation (MBE) to analyze the field data before and after CO2 injection. After matching the historical field data the proposed model can be applied to evaluate, monitor and predict the overall reservoir dynamic performance during CO2 flooding process. In order to accurately account for the complex displacement process involving compositional effect and multiphase flow, the PVT properties of reservoir fluids and the four-phase fluid relative permeability relationship are integrated in the model. This study has investigated the effects of a number of factors, such as the reservoir pressure, the amount of CO2 injected, the CO2 partition ratios in reservoir fluids, the possibility of the existence of free CO2 gas cap, the proportion of reservoir fluids contacted by CO2, the starting time of CO2 flooding, the oil swelling, and the oil relative permeability improvement when mixing with CO2, etc. This study has shown that the proposed MBE model is an effective complementary tool to analyze/monitor the overall reservoir performance in tertiary CO2 recovery process. The model has been applied to analyze the Weyburn CO2 flooding project as an example. MBE analysis also indicated that:there exists a free CO2 gas cap under reservoir condition even if the reservoir pressure is larger than MMP in the Weyburn oil field,the CO2 partition ratios in oil, water and gas phases and the proportions of reservoir fluids contacted by CO2 affect largely the drive mechanism and production performance, further experimental study is recommended,a higher oil recovery can be obtained when CO2 flood is initiated at an early life time of the reservoir, andthe effect of CO2 solubility in water under actual reservoir condition cannot be neglected. The proposed new model is the first one in developing and applying MBE to evaluate the overall dynamic performance for CO2 flooding process and a valuable insight into reservoir responses during this process has been attained. Introduction CO2 flooding is considered one of the most effective tertiary recovery processes in light/medium oil reservoirs and has achieved widespread use in petroleum industry. However, the complicated displacement mechanisms involved in the CO2 injection process have not been completely understood. Monitoring the reservoir performance and obtaining the accurate information regarding the reservoir fluid and injected fluid using actual field data will help to understand the mechanisms and manage the CO2 injection project efficiently. There are two types of methods that can monitor and evaluate reservoir performance: numerical simulation and MBE. MBE is a classic reservoir engineering tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".